15 Topics
5 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- Agile and Scrum Practitioners
- Business Analysts
- Operations Managers
- Team Leaders and Supervisors
- Risk Managers
- Project Controls Professionals
- Digital Transformation Professionals
- Technology and Innovation Managers
- Consultants and Project Management Advisors
- Professionals seeking to integrate AI into project delivery
Session Objectives
- Understand the core concepts of Workday Project Management
- Navigate the Workday interface for project-related tasks
- Create and configure new projects
- Manage project teams and resources
- Track project progress and time
- Understand project costs and financials
- Create and run project reports
- Use Workday for project-based billing
- Collaborate on projects within Workday
- Configure project security and roles
About the Course
The Innovate & Automate: AI in Project Management Tools Training Course equips project professionals with practical knowledge and skills to leverage artificial intelligence within modern project management platforms. Participants will explore how AI can automate routine tasks, improve project planning, enhance decision-making, streamline communication, identify risks, and optimize project performance.
The course focuses on practical applications of AI-enabled project management tools, including intelligent scheduling, task automation, predictive analytics, resource optimization, AI-assisted reporting, collaboration, and project monitoring. Participants will also examine responsible AI adoption, data security, governance, and the limitations of AI in project environments.
Curriculum & Topics
15 Topics | 75 Sessions
-
Workshop 1.1:
What is AI and how does it apply to projects
-
Workshop 1.2:
The history of AI in business
-
Workshop 1.3:
Understanding the difference between ML and AI
-
Workshop 1.4:
The benefits of AI-powered project tools
-
Workshop 1.5:
A look at the current market of AI PM tools
-
Workshop 2.1:
How AI optimizes project timelines
-
Workshop 2.2:
Using AI to create project schedules
-
Workshop 2.3:
Dynamic scheduling based on real-time data
-
Workshop 2.4:
The role of dependencies and constraints
-
Workshop 2.5:
Handling unexpected delays and changes
-
Workshop 3.1:
Optimizing resource utilization with AI
-
Workshop 3.2:
Matching team members to tasks based on skills
-
Workshop 3.3:
The challenge of resource contention
-
Workshop 3.4:
Predicting resource availability
-
Workshop 3.5:
Using AI to balance workloads
-
Workshop 4.1:
What is predictive analytics
-
Workshop 4.2:
Forecasting project timelines and milestones
-
Workshop 4.3:
Predicting budget overruns
-
Workshop 4.4:
Identifying potential roadblocks
-
Workshop 4.5:
Using historical data to inform future decisions
-
Workshop 5.1:
How AI identifies project risks
-
Workshop 5.2:
Assessing risk severity and probability
-
Workshop 5.3:
Creating a risk mitigation plan
-
Workshop 5.4:
The role of sentiment analysis in risk detection
-
Workshop 5.5:
Using real-time data for risk monitoring
-
Workshop 6.1:
Generating automated status reports
-
Workshop 6.2:
Creating dashboards with real-time insights
-
Workshop 6.3:
The benefit of natural language generation (NLG)
-
Workshop 6.4:
Customizing reports for different stakeholders
-
Workshop 6.5:
The future of project reporting
-
Workshop 7.1:
Automating routine and repetitive tasks
-
Workshop 7.2:
Using AI to trigger actions
-
Workshop 7.3:
Setting up simple automations
-
Workshop 7.4:
The concept of smart workflows
-
Workshop 7.5:
The role of low-code/no-code platforms
-
Workshop 8.1:
AI assistants in team communication
-
Workshop 8.2:
Summarizing meeting notes with AI
-
Workshop 8.3:
The role of chatbots for team support
-
Workshop 8.4:
Analyzing team communication patterns
-
Workshop 8.5:
Improving collaboration with AI-powered tools
-
Workshop 9.1:
Using AI in sprint planning
-
Workshop 9.2:
The role of AI in backlog refinement
-
Workshop 9.3:
Automating sprint retrospectives
-
Workshop 9.4:
Predicting story points and velocity
-
Workshop 9.5:
The future of AI in agile methodologies
-
Workshop 10.1:
AI-driven budget planning
-
Workshop 10.2:
Predicting project costs
-
Workshop 10.3:
The importance of real-time financial data
-
Workshop 10.4:
Integrating with financial systems
-
Workshop 10.5:
The role of AI in project ROI
-
Workshop 11.1:
How to evaluate AI PM tools
-
Workshop 11.2:
Key features to look for
-
Workshop 11.3:
The importance of tool integration
-
Workshop 11.4:
A comparison of popular AI-powered tools
-
Workshop 11.5:
Making a business case for a new tool
-
Workshop 12.1:
The importance of data quality
-
Workshop 12.2:
The ethics of using AI in projects
-
Workshop 12.3:
Understanding data privacy and security
-
Workshop 12.4:
The challenge of data bias
-
Workshop 12.5:
The role of responsible AI
-
Workshop 13.1:
Using AI to optimize a project portfolio
-
Workshop 13.2:
The role of AI in portfolio selection
-
Workshop 13.3:
The importance of aligning projects with strategy
-
Workshop 13.4:
Reporting on portfolio performance
-
Workshop 13.5:
The future of PPM
-
Workshop 14.1:
A case study on a successful AI implementation
-
Workshop 14.2:
A failure analysis of a poorly implemented tool
-
Workshop 14.3:
The impact of AI on project outcomes
-
Workshop 14.4:
The benefits of a data-driven approach
-
Workshop 14.5:
Real-world applications of AI in projects
-
Workshop 15.1:
The next wave of AI in project management
-
Workshop 15.2:
The role of generative AI
-
Workshop 15.3:
The integration of AI with the metaverse
-
Workshop 15.4:
The future of the project manager
-
Workshop 15.5:
Keeping up with a rapidly changing landscape
